Multistage anxiety state recognition based on EEG signal using Safe-Level SMOTE

Abstract

Anxiety is a complicated emotional condition that has a detrimental effect on people’s physical and mental health. It is critical to accurately recognize anxiety levels in early stage. The anxiety can be detected by pattern of brain signal using brain imaging tools. However, the common problem with dataset acquired from brain is imbalanced class distribution. Hence, the purpose of this work is to mitigate the imbalanced class distribution issue by removing data outlier and using improved Synthetic Minority Oversampling Technique (SMOTE) for improving the classification performance. This work used of the freely accessible Database for Anxious States based on Psychological stimulation (DASPS) that comprises of 14 channels electroencephalography (EEG) signal. It acquired from 23 subjects when they were exposed to psychological stimuli that elicited fear. The DASPS need to be processed for removing noises, extracting important features and sampling with Safe-level SMOTE method. Then, the processed DASPS was categorized into three types of model: Model A, Model B, and Model C. The feature Model C from enhanced DASPS class distribution obtained the precision of 89.7% and accuracy of 89.5% using optimized k-nearest neighbour (k-NN) algorithm. The proposed method showed outstanding classification performance than others existing methods in recognizing multistage anxiety.